In business and society we are taught about the importance of individual motivation. When business planners try to identify appropriate motivational levers to provoke desired behaviors, they usually consider a trade-off between the positive, the 'carrot', and the negative, the 'stick'. The donkey analogy is commonly used to illustrate this situation. Our goal is to get a donkey to move, and we craft our options based on the psychology of motivation, thus resorting to the tasty carrot or the nasty stick.

And yet the donkey rarely finds himself alone. There are often other donkeys in the field. These other donkeys provide a powerful reference frame for the donkey we are trying to persuade. In a business situation the equivalent of 'the other donkeys' is also a motivation in itself, quite possibly overpowering any individual motivational factors that might be present.

The growing use of behavioral psychology is bringing benefits in terms of more human solutions to tricky problems. However, we believe that the focus on individual psychology is mistaken. We do not need to understand or address the motivations of the vast majority of people on the level of the individual. Instead we need to revisit our definition of motivation and add in a separate social element: people follow other people (or donkeys).

The good news is that this rule greatly simplifies the world and reduces the complexity of the problems we face in human persuasion. Donkeys follow, people follow. And with that rule we can start to design solutions that spread and scale more effectively.

Interest is growing in understanding how ideas, behaviors, and products and services spread.We have now designed a variety of Learning Formats to introduce you to the Science of Spread™.

Spread is a specialist discipline, a new discipline, one that can have a huge positive impact for all organizations. Even if some of the techniques can be guessed, it is complex and broad enough to require specialist insight and knowledge. Our Learning Sessions are designed to provide individuals and organizations with the knowledge they need to design effective spread programs, based on solid scientific understanding and real-life cases.

Yes, you can keep muddling your way through. But, how much better would it be to know what you are doing?

Give us a call if you are looking for a workshop or lecture for an internal event, a conference or a university course, delivered by an experienced and engaging speaker.

- “Keep doing what you are doing until there is a compelling reason to change"

These are two very important, deeply simple rules of thumb, or heuristics, that help us as humans living in a complex world survive every moment of our existence. We use these heuristics as individuals to work out what we should do in a given situation. There are lots of heuristics (another one is the crucial "don't bump into things") and research indicates that mostly we use a 'best fit and satisfice' method of heuristic selection. That means simply: run down the list of heuristics, and stop when you have found enough to make sense to keep on living.

These two heuristics therefore explain all kinds of behaviors, from queuing to massing in the streets (or not – as 'not massing' is also a behavior pattern that can be followed).

Now an interesting question to work out is how anything new gets started. After all, if everyone keeps doing what they are doing, why would anybody ever change?

One might start at the concept of deviance, whereby one or more people – let's call them 'people' to be simple – exhibit deviant behavior from the rest. They might do this for any number of reasons, from being mentally ill to misunderstanding what people are supposed to be doing. Either way, irrespective of their inner workings, their behavior may deviate from the normal pattern, and in this way they are 'deviants'.

One might think that 'young people start things'. Well, there is some truth to that, but note that by and large very few things get picked up by anyone else, and so the fact that occasionally a young person may start a trend would not be a compelling explanation.

A phenomenon that actually allows you to study how things really start (before we get into mushrooms) is a standing ovation. Simply put, standing ovations do not and cannot start at the back of the room. This is the simple secret to contagion in its very early stages.

Standing ovations cannot start from the back. They cannot start from a mile outside the auditorium. They actually can ONLY start from the front of the room. And they rarely ever start in the middle of the performance ("it's just not done").

The reason for starting at the front is obvious. A person trying to kick off a standing ovation from the back is totally ignored, not because they misunderstood the performance, but because not enough people can see their actions. In contrast someone who stands up at the front, at the appropriate moment, is noticed by lots of people (even if they are just trying to escape the theater early to pick up their valet car or coat before everyone else).

A standing ovation works like momentum spreading through a network. In this case, each person functions as a 'node' in a network. The network shape of an auditorium has rows and rows of nodes, stretching back, and up if the space is big enough. Now think of each node having either a passive or an active state. Everyone starts off in a passive state. The seating in the theater creates the structure of the network, but it takes some energy to agitate the nodes to turn them into active nodes, i.e. those that take part in the standing ovation.

Now that we have a concept of active and passive nodes, in the first moment – let's call it Ignition – one node will become active. As we are studying a standing ovation, by necessity (and design) this node needs to be in the front few rows. Which node? We'll cover that later, that will be the piece about mushrooms – and maybe the randomness of quantum physics. Whatever the reason, all of a sudden, one node is activated and we have ignition.

The activity spreads through the network in something I'm calling Pick-Up Waves. Generally there seem to be two distinct Pick-Up Waves, 1 and 2. After that, there will be a new phenomenon to look at, but we're not there yet.

Pick-Up Wave 1 starts due to the heuristic we used at the beginning of this piece - "follow what your peers are doing". It also uses a variant of the same, potentially deviant, behavior that sparked the initial node into life. In both cases, more nodes get activated, typically in some kind of proximity pattern to the original ignition (front rows, but not always next to the ignition node). Not necessarily masses of nodes, but enough to form a wave that is visibly spreading.

But wait- not all pick-up waves are positive! It would be a mistake to assume that one person standing up will always encourage others to do the same. It turns out that nodes can activate or remain passive. If they remain passive, this is effectively a form of 'anti-action', and the resulting pick-up wave is one of non-action. This is an important point, as the rules of following still apply, it is just a 'negative follow'.

Pick-Up Wave 2 is similar to Pick-Up Wave 1, except that there are more people now standing (or not, for a negative pick-up). The positive Pick-Up Wave has more mass, and there will be some 'node jumping', with people further back and on different sides of the room standing up. Each, by the way, may be standing up for their own reasons. And each may deny that they stood up because they saw other people doing it. Nevertheless, now we have a full blown pick-up wave in force.

Have you ever noticed that all of a sudden the room goes from a spotty standing ovation to everyone and their granny standing up? (apart from granny sometimes – more on that later). At the ignition phase and the pick-up waves, the driver of human behavior was primarily emergent, totally voluntary, with a good degree of random activity thrown in. After the second pick-up wave, the story changes, and the system goes from being emergent to structured. Now a new rule takes over: "I need to see what is going on".

There are now sufficient people standing up to make it nigh on impossible for most other people to be able to see. And so, even if they did not think the performance was that great, they find themselves standing up, en masse, in a wave that propagates to the back of the room. It is worthwhile saying, just for completeness, that the wave does not keep going indefinitely. Obviously there is a wall between the auditorium and the street, but it does highlight that there are constraints to how far any wave can propagate.

Note that this system is practically non-voluntary. This is made even clearer by the fact that those people who do not stand up instead end up in isolation. Usually they have a very good reason not to stand, such as a broken leg or being in a wheelchair (hence the granny reference above). At the same time it is obvious that they are not forming part of the group behavior, and in some way they are 'bad' as they do not conform to the new normal behavior.

Thus we can use the standing ovation to work out how things spread, quite simply, a micro-second at a time.

Which leads us finally to mushrooms, the secret to ignition.

For some reason, mushrooms always tend to sprout up, seemingly out of nowhere, in the same place over and over again. It might not be in exactly the same place, more 'thereabouts', but close enough for you to remember.

Mushrooms are fungi. In order for the fungi to sprout as a mushroom (note: I am not going into advanced biology here, it is a metaphor), the spores need a substrate to grow on in addition to enabling nutrients (soil). They sprout out when there is a catalytic reaction (heat and sunshine), and out pops a lovely mushroom. The key element in the case of ignition is that the spores are on the substrate, appropriate for the mushroom, and the nutrients are in place.

A standing ovation starts from the front of the room because, in the world of biology, that is the substrate and best nutrient environment for the wanna-be mushrooms. People who like a performer might book a ticket earlier, guaranteeing them a good spot. They might be willing to pay more money. They may even 'prime' themselves during the performance that the evening was amazing, so they are ready to sprout…

We just don't know which mushroom – or now we go back to 'node' – will activate first. We just know it has to be one that sits in the front of the room. There is a certain spookiness to the random behavior of the nodes at the front. We know that one will ignite, but have no idea which one. In the same way we really do not know how the Pick-Up Waves will work, positively or negatively, and which of the nodes will activate. We do know that there is an imperative to have practically everyone else follow in the end, irrespective of desire, but these first batches in the path to contagion are random.

I'll work more on random and the spooky nature of random connections in other papers (I have already written some on the spooky nature of abundance). Suffice to say here that it does have 'knowable' properties, especially if you look at the picture from the whole perspective and not fixate on any individual node.

Hopefully this paper / blog post will be useful to you. It is a simplified way of looking at how things like the Wikileaks release of the State Department cables can spark revolutions in Tunisia and Egypt. And hopefully it will get you started looking at the world in a slightly different way.

Try it, next time you are in an auditorium, clapping or being in a standing ovation. And you'll start seeing the world with a new lens.

8. Mass concentrated reaction – mass events, close down major areas of social, political, industrial activity. The upgraded system response serves to annoy everyone, even though the actions are only intended to affect a small group. Now everything scales up and concentrates (1M march through Cairo, etc).

9. Weak palliative response – failing regime makes an attempt (or several) to reduce the pressure on the core (i.e. fire government, appoint new VP, say that you will leave in less than 5 years, etc)

10. Increasing mass reaction & unlikely system response – harder / impossible to stop 'everything', military action tough (well 'designed' regime changes brings the military on side, with the argument “Egyptians cannot and will not kill Egyptian women, children, mothers and daughters”). Regime therefore loses its fall-back support (which in reality, we now know, could never save them anyway, save in the harshest of circumstances).

12. 3-Day chaos – during this time, no one knows what is going on, reaction and system are in a holding zone

13. Transition formation – developing the state change to 'something'

14. Transition reaction – takes a while to formulate, but as Tunisia has found, a transition system does not gain automatic acceptance

15. Transition acceptance / rejection – based on the level of acceptance, can continue the reactions for regime change (at large scale, because protest movement is still in place), or begin to mellow out and regain sense of (new) normal activity

Within this particular branch of probabilities, we have the classic Birthday Problem: What is the % likelihood that two or more people share a birthday in a group of 30 people?

Well, I decided to see how things work on a large sample of people and dates - that being my own Facebook account. It occurred to me as I was writing “Happy Birthday” on people’s walls that sometimes there are two or three people with birthdays on the same day. So I decided to plough through the list and see what I could find out.

On 36 / 365 days (10%) three people shared a birthday – that being three “pairs”, i.e. each of these people having a match with each of the other two.

On 5 / 365 days (1.4%) four people shared a birthday – that being six “pairs”.

On 1 / 365 days (0.3%) five people shared a birthday – that being 12 “pairs”.

So on 33.7% of the days, at least two people shared a birthday.

It seems that there are some days that are more "popular" for birthdays than others. This real-life phenomenon is not taken into account in the classic problem which assumes that all days have an equal probability.

Unfortunately my math is not quite good enough to do the fun stats, like taking different groups of 30 people amongst my friends and looking at their matches. I am looking at using freelancers on various web sites to help me out. I’m also posting my spreadsheet here so if someone fancies having a go at the data set, they are welcome!

In the meantime, I will make sure I remember to say happy birthday to someone in my group at least once every third day.